Abstract
Sentiment analysis can go beyond the typical granularity of polarity that assumes each text to be positive, negative or neural. Indeed, human emotions are much more diverse, and it is interesting to study how to define a more complete set of emotions and how to deduce these emotions from human-written messages. In this book chapter we argue that using Plutchik’s wheel of emotions model and a rule-based approach for emotion detection in text makes it a good framework for emotion classification on social media. We provide a detailed description of how to define rule-based patterns for Plutchik’s wheel emotion detection, how to learn them from the annotated social media and how to apply them for classifying emotions in the previously unseen texts. The results of the experimental study suggest that the described framework is promising and that it advances the current state-of-the-art in emotion detection.
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Notes
- 1.
The Association for the Advancement of Affective Computing—http://emotion-research.net/.
- 2.
Note that the examples only list words but a pattern can consist of any combination of words and POS-tags. This concept is further explained when we describe how to learn a model.
- 3.
- 4.
The labels are Sorry, Hugs, You Rock, Teehee, I Understand and Wow, Just Wow.
- 5.
Can be found at https://github.com/sancha/jrae.
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- 7.
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This research is partly supported by Data Science Center (DSC/e) of TU Eindhoven.
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Tromp, E., Pechenizkiy, M. (2015). Pattern-Based Emotion Classification on Social Media. In: Gaber, M., Cocea, M., Wiratunga, N., Goker, A. (eds) Advances in Social Media Analysis. Studies in Computational Intelligence, vol 602. Springer, Cham. https://doi.org/10.1007/978-3-319-18458-6_1
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